Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Metric Nearness Made Practical
Authors: Wenye Li, Fangchen Yu, Zichen Ma
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In empirical evaluations, the proposed approach runs at least an order of magnitude faster than the state-of-the-art solutions, with significantly improved scalability, complete conformity to constraints, less memory consumption, and other desirable features in real applications. |
| Researcher Affiliation | Academia | Wenye Li1,2, Fangchen Yu1, Zichen Ma1 1 The Chinese University of Hong Kong, Shenzhen 2 Shenzhen Research Institute of Big Data 2001 Longxiang Boulevard, Longgang District, Shenzhen, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: The Proposed HLWB Algorithm |
| Open Source Code | No | The paper refers to existing implementations of other algorithms (e.g., "2https://optml.mit.edu/work/soft/metricn.html", "3https://github.com/rsonthal/Project And Forget", "4https://github.com/spitis/deepnorms") but does not provide a link or explicit statement about the availability of the code for *their own* proposed methodology. |
| Open Datasets | Yes | Besides, the real MNIST dataset (Le Cun et al. 1998)7, which consists of grayscale images of hand-written digits, was used in the experiment. |
| Dataset Splits | No | The paper mentions generating artificial datasets and using the MNIST dataset, but it does not specify the train/validation/test splits, only that "n = 100/500/1,000/1,500 nodes were artificially generated" and "n varying from 100 to 1,500" for MNIST. |
| Hardware Specification | Yes | Most experiments were executed on a conventional server with a single CPU (intel Xeon 8180) enabled, except the Deep Norm algorithm that ran on a deep learning platform. |
| Software Dependencies | No | The paper mentions specific solvers like CPLEX and MOSEK, but does not provide their version numbers. It also links to open-source implementations of compared algorithms, but without stating specific software versions for those. |
| Experiment Setup | Yes | The measurement matrix Do was formed by adding random noise to the elements of Dg, with each do ij = max{0, dg ij + ฮถ ยท mean(Dg) ยท N(0, 1)} where ฮถ = 0.5 and 0.8 respectively and mean(Dg) denotes the mean value of all entries of Dg. |